{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load libraries\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# set the filepath and load in a shapefile\n",
    "fp = \"datasets/geo-data/gis-boundaries-london/ESRI/London_Borough_Excluding_MHW.shp\"\n",
    "\n",
    "map_df = gpd.read_file(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>GSS_CODE</th>\n",
       "      <th>HECTARES</th>\n",
       "      <th>NONLD_AREA</th>\n",
       "      <th>ONS_INNER</th>\n",
       "      <th>SUB_2009</th>\n",
       "      <th>SUB_2006</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kingston upon Thames</td>\n",
       "      <td>E09000021</td>\n",
       "      <td>3726.117</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((516401.6 160201.8, 516407.3 160210.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Croydon</td>\n",
       "      <td>E09000008</td>\n",
       "      <td>8649.441</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((535009.2 159504.7, 535005.5 159502, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bromley</td>\n",
       "      <td>E09000006</td>\n",
       "      <td>15013.487</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((540373.6 157530.4, 540361.2 157551.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hounslow</td>\n",
       "      <td>E09000018</td>\n",
       "      <td>5658.541</td>\n",
       "      <td>60.755</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((521975.8 178100, 521967.7 178096.8, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ealing</td>\n",
       "      <td>E09000009</td>\n",
       "      <td>5554.428</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((510253.5 182881.6, 510249.9 182886, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   NAME   GSS_CODE   HECTARES  NONLD_AREA ONS_INNER SUB_2009  \\\n",
       "0  Kingston upon Thames  E09000021   3726.117       0.000         F     None   \n",
       "1               Croydon  E09000008   8649.441       0.000         F     None   \n",
       "2               Bromley  E09000006  15013.487       0.000         F     None   \n",
       "3              Hounslow  E09000018   5658.541      60.755         F     None   \n",
       "4                Ealing  E09000009   5554.428       0.000         F     None   \n",
       "\n",
       "  SUB_2006                                           geometry  \n",
       "0     None  POLYGON ((516401.6 160201.8, 516407.3 160210.5...  \n",
       "1     None  POLYGON ((535009.2 159504.7, 535005.5 159502, ...  \n",
       "2     None  POLYGON ((540373.6 157530.4, 540361.2 157551.9...  \n",
       "3     None  POLYGON ((521975.8 178100, 521967.7 178096.8, ...  \n",
       "4     None  POLYGON ((510253.5 182881.6, 510249.9 182886, ...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data type so we can see that this is not a normal dataframe, but a GEOdataframe\n",
    "map_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11853c860>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVgAAAD8CAYAAAAylrwMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzsvXd4Y2eZ9/856tUq7t2eXuMpnpkQ\nAgkhjexCKCG7vPtCEkJdYOFHX3bZd5e+u7CUpQYICZ1AgIQSSiA9k5nx9OoZj3svktW7nt8fkh0X\nyZZkFc/kfK4rVzxH0jmPZel7nud+7vt7S0IIZGRkZGTyj6LUA5CRkZG5XJEFVkZGRqZAyAIrIyMj\nUyBkgZWRkZEpELLAysjIyBQIWWBlZGRkCoQssDIyMjIFQhZYGRkZmQIhC6yMjIxMgVCVegD5pqKi\nQrS0tJR6GDIyMpcxhw8fnhRCVC73vMtOYFtaWujo6Cj1MGRkZC5jJEnqy+R5cohARkZGpkDIAisj\nIyNTIGSBlZGRkSkQssDKyMjIFAhZYGVkZGQKhCywMjIyMgVCFlgZGRmZAiELrIyMjEyBuOwKDWRk\nAELRGL86MsQjp0aJxQXbGyzsbbFz1bpytColkVicSCyOQSN/BWQKx7KfLkmSGoHvAzVAHLhHCPFl\nSZLswM+AFqAXuF0I4ZQkSQK+DNwC+IE7hRBHkue6A/jX5Kk/JYS4P3l8N3AfoAd+D7xXCCHSXWPF\nv7XMZUMwEsPhC6NUSJh1KoKROE+en+BLj56nd8o/+7ynuyb5BhdRKiRMWhX+cJRoXHD1ugpu3VHP\n63bVk/joysjkD2m5rrKSJNUCtUKII5IkmYHDwKuBOwGHEOJzkiR9FLAJIT4iSdItwHtICOw+4MtC\niH1JsewA2gGRPM/upCgfBN4LPEdCYL8ihHhEkqT/SnWNpcbb3t4u5FLZy4PuCS/nRj28eG0FFoN6\n9ng8LvjNiWHue7aXYwPT5KMx8s4mK//2t1vY2WRb+clkLnskSToshGhf7nnLzmCFECPASPJnjyRJ\nZ4F64Fbg2uTT7gceBz6SPP59kVDu5yRJsiZF+lrgz0IIR3KAfwZuliTpcaBMCLE/efz7JAT8kSWu\nIfMCoGfSxz/+6AgWvZrX7WqgrdHCqCvIQ8eGOTPizuu1jvZP87pvPMtbX7qGd16zFqtBA4AQghFX\nkI4+J7ubbdSW6VAo5JmuTGZkFYCSJKkF2AkcAKqT4osQYkSSpKrk0+qBgTkvG0weW+r4YIrjLHGN\nheN6G/A2gKampmx+JZlVzIyQuQIR7n2mp+DXiwv41hPdfPepHjbVmtGplHRP+nD4wrPP0agUmLUq\nmssNKCQJlVJic20Z2+strKk0oVMrUCsVrK00FXy8MqufjAVWkiQT8CDwPiGEe4l4VaoHRA7HM0YI\ncQ9wDyRCBNm8Vmb1MujwL/+kAhCNC04NpZ4hh6NxpqJhpuaI7nPdjnnPkSQ4/R83yRtoMpmlaUmS\npCYhrj8SQvwyeXgsufSfidOOJ48PAo1zXt4ADC9zvCHF8aWuIXOZ4w5G+NKjF0o9jJwQArrGvaUe\nhswqYFmBTWYFfBc4K4T4nzkPPQzckfz5DuChOcffJCW4EnAll/l/BG6UJMkmSZINuBH4Y/IxjyRJ\nVyav9aYF50p1DZnLnK8/dnHeLPFSQ6OSU8xlMgsRvBh4I3BSkqRjyWMfAz4HPCBJ0t1AP/D65GO/\nJ5FB0EUiTesuACGEQ5KkTwKHks/7xMyGF/BOnk/TeiT5H0tcQ+YyZsQV4HtFiLkWkmqzrtRDkFkF\nZJJF8DSp46QAL0/xfAG8K8257gXuTXG8A9iW4vhUqmvIXL6MuYP89x87CUXjpR7KijDp5PirjFzJ\nJbOKeKZrkrvvP0QwcmmLK8CpIZecUysjexHIFI9QNMYDhwZIVdxyYczDu3585LIQV4AaixwikJFn\nsDJFIBqL4w1F+cbjF/n54UE6xzy8qq2OtkYrT5yf4Kt/ucCUP8y0P1LqoeYNtVKeu8jIAitTBJ66\nMMm//Ookw64gAPc+08NdL24hFhf820OnUCskuid8JR5lfhlw+KkwaUs9DJkSI99mZQrOtRsrqTA/\nLzZCwDX//Tiv/+az9E35sRo1JRxdYfj335wp9RBkVgHyDFamoOy/OEUwEsMfjs07HosLjvRPA9A3\n6Wdvix1XMELnqKcUw8w7StmuQAZZYGXySDASwxeKUm7S8uczY/zl7BgPdAwQX6Z4ecIbYsIbAsBu\n0LC2yogvFOXcqGfZ165W5AwCGZAFViYP/OHUCI93TvBc9xTXbqyi3qrnM4+czclG0OEP4+hNVHCZ\ntEo215ZxdsSDNxTN86gLyy5ZYGWQBVYmB8Y9QQwaFb87MczDx4fZf3FqdqZ537O9ebuONxTjUK8T\nhQQ7Gy2cGHITu0SmtBtrzKUegswqQBZYmaw5NeTiXT86SiASW/7JeSAu4OiAiyqzlnFPqCjXXCm+\nS2zGLVMY5CwCmayxG7VFE9e5tFQYi37NXNGq5a+WjCywMjkw5AyU5Lpnhl1UmC6NlK6jfU7ODOe3\n64LMpYcssDJZ0zNZGq/TOqueSe+lYWH48YdO0z3pRQhBIFz82b7M6kCOwcpkzeOdEyW57qg7yJ4W\nG4d6V3djYb1aQSAS58O/OMGXHr2ALxTl/jfvRa1UoFUpqDRrZ0tphRByN9vLGFlgZZZkeDpA75QP\nvVrJziYbR/uddPSVRuDcgSijriDb6y2cHHKVZAyZYNapCURC+MOx2c4GN37xydnHtSoFe1vtVJq1\nDDoDPPD2F5VqqDIFRhZYmbQc6XfypUcv8OT5CdZWGnnntev46IMnSjqmAWeAAWeAHY1Wjg1Ml3Qs\n6agqWzrbIRSN89SFSQAsejVd4x7WVclpXZcjcgz2BYA/HE1pEbgUY+4gr/36szx5PhEOuDjh44M/\nP050leSh9jv8WPTqUg8jJQZ15vMWVyDCQ8eGl3/iZUQ4GidYgiyUUiDPYC9z4nHBG797EI1SwVtf\n2so1G6pQKlLH/MLROM9enGTA4S9ZnDVTHL4wzXYDRq2S4elgqYczi1KCnqnsnMHG3ZdGbm8+GJ4O\n8IU/nWfA4eeeN+3Garg0skJyRRbYyxSHL8z39/fy2xMjs3HA/d1T1Fv1vP+G9Vy1roJai55ILM7v\nT44w5Q3zq6NDqzq2uZA+hx+NUmJNhZHuydVhd7ijycrhvuxCFzdsqS7QaFYPvlCUrz3WxTefuDhb\n9XfLl5/i3rv2sKmmrLSDKyCywF6GPHtxknf/+CiOFF1Zh6YD/OC5fj74ixOYNCo0KsUl3b01HBNM\n+ULsbbFxcBVkF0x4snsvN1SbuHp9RYFGUxrC0fhsV92Tgy6+83Q3h3ocs37AMwy7gtz+zf384O59\ntDVaSzHUgiML7GXG8YFp7r6vY8lKK7VSQgjwhKJwGaxOXYHoqnDduqLewoksVwAGjYqfHRrgjqta\nCjOoAiKE4LHOcT78ixNcuaacYCTGySEXY+4QG6vNqJQSp5cptnAHo7z+m/v5l7/ZzBuvbEaRJnx1\nqSJlu/mx2mlvbxcdHR2lHkbR8QQjhKJx/vYrTzPqXjomadGrMGhUjLhWT+xyJagUYDNqmSixT8Gm\nGjPnsvCzVSskNCoFvnCMt790DR99xaaS5cSOe4L88sgQaoVEXIBSISFJiU24aEzQaNdTXaZjbaWJ\nequeXx0d4n/+fJ6h6fxV9bU32/iv265gTaUpb+csFJIkHRZCtC/3PHkGm6Rn0scDHQO8ekf9JeeE\ndGbYzTt+eJhwNL6suEJixldp1qKQWBUzv5Wys6n0xQfZiitAc4WBrvFE7PhbT3ZjM2p4+0vXFF1k\nHzo2xCd/e5ZJb4jdzTYOL5PnXGfRLVru54OOPic3f+kp7n5JK/903Xr0GmXer1Fs5DStJJ//Yyff\nePwiN33pSQ72OEo9nIwYdwf594dP88qvPk2/w5+RuM7QNe6jvdlewNEVj9WQOmbSZjdX0SglIrH5\n4/7cI+d48MhQPoe1LN97pocPPHCcyaTh+eE+J1vrlt50KoS4zhCOxfnG4xe59WtPM+IqjedFPpEF\nNsk/7GtiZuLwyd+eYdq/Ojd++qZ8fPaRs9z8pSfZ+5m/cN+zvTl7pB7sdbCr6dLeXNAopdksiVKx\ntc6ccXWbQoK1lUZ2Ntnom/IvevxjvzzJ0f755wqEY4zmUdTiyc9L56iHzz1ybtENyhOMUupQ6Pkx\nL3fee4jxLCYNqxFZYJPUWHSzDvwnh1zc8uWn2H9xqrSDWoA3FOWu+w7xrSe6s16OpuP8mIfqsku3\n++nWegueYOm8V+0GNeNZZA7EBbPj1aoWf/3CsTjv/vFRLox5+M5T3dx93yHu+N5Bfn9yJOuxhaPx\nRQUmT56f4KYvPcnXH+/i1q89TSgaX/S6fod/2VlsMegc8/Afl3jzSHmTK8l7fnKU3xxfXFFz51Ut\nfODGDZh1pa0aGnT6+dDPT7C/O/+iv67SSJ/Dv2jJutpZDcYvK/FFaG+2pZ35alQKwtE4Ro2Sg/9y\nPcYsQxCPnRvn4w+d4l9u2czuZhuhaJxvPnGRHx3oz+j1LeUGelPMsEvB51/fxm27G0o9jHnIm1xZ\n8optNTzXPbVoJ/q+Z3vZf3GKn739yiWrTqKxOA5/mNPDbtZVmmi0G/IyrjF3kG890c33nu3JqcdV\nJnRN+FaFWGXD+mpTyf1WN1SbVlSY0dHnTOupEE7OLKNxwX/+4RwfvnlTVnFevUbJoDPAP/30KApJ\nIhyLZ/X56Z3yU2vRrYpMk4/98iQGjZJbtteWeihZIwtsklu216JUSLz9B4cXPdY55uGOew/y7Tva\nqTLrUr7+gY5BPvark0AixeVlG6u4co2dV2yvpd6qz2oskVic+57ppaPPweOdEymXcfnmUK+TKxos\nnBi8NCq5hBAl6aowl3yUecbiS/9tQ9E439/fx9Ndk/zvG3aytc6S0XlnBDqxKsntzlxn1ZdcYCtM\nWj544wZ2XqJ7BbLAzmGpPMrjgy7e9N2D3PPGdprK589OhRA8eGRw9t+xuODRs2M8enaMzz1yDrMu\n8TavrzbzkZs3sjvF7v2YO8iBHge9kz5+e2KY82PF37g5M+xa9VaAM3SN+9jXaudAjwObQY3THyn6\nGPryUJ6rUWWWitQ94ePWrz7De65bz3uuW7dsQv7//Pn8iseWzrOimLyyrZa/39tU6mHkjCywc3jk\n1NIbCedGPdz0pSd593XrePOLW9FrlEx6Q/y/h06nzR2MxsXsl/9gj4O/+9Zz3H11Ky0VRprLDYQi\ncR4+nujOWuqOqdE49E75qDBpVn3nAIlEWKa1wkjPpI8ttWUYtUqEgGMDTmYm/epkOpRZq0StVFJV\npsGsUyOAaEwQjsZRKEAhSSglCbVKwVxZicUF0XicWFxwcmh+SKKlwsjYCoobtCqJ81lsVkbjgi8+\nep6Hjg9xe3sjr9lZT5VZy73P9HLv0z1sqjHzwZs2EosL/OGVb/yVWl7XVZl498vWlXgUK0Pe5EoS\nisZo+48/EYxkthyvMmt5ZVsdvz46lHMt/7b6Mk4Nrb6+TVtqyzg76i5YzDcfrKs00jWRfgapUyuI\nxASxuEClkIjGBZLEin6nepueequeSDSONxTlwgrTwzJJ6l8KtVKi0WaYZ3RjN2oIhGN5CZ+0NVo4\nPlC61Uy9Vc+OJis3b63hRWvLsRs0q6aUVt7kypIBRyBjcQUY94T47tM9K7qmc5WarJwZcbO3xc7B\n3tVTcKFSwNxQ9PB0AJ1akfZvNvf4TJ7nSm8YQ84AQ84Ae1vsKxZXAIdvZaW9kZhY5CKWyuAnVzTK\n0mZxDk0HGJoO8LsTiZWlJMEHb9zIuy6hWa2cB5ukFGV5Q9NBGu3ZbYAVi4O9DjZWr56S4Z1NNva2\nPB+79kfiWW8e5gOLXs2Z4ZXP6jbVmOiZXB1pUKnY1WRFtUpmizMIUXrRz5ZLa7QFpM6io8pc/IR7\nxSpueDfhCZXkPUnFhCfEuOf5He31VSYuLhEiKBQbqk1489Al1lTivOqlaGu0cGxgmkAkht2wusZp\nN15aBt3LCqwkSfdKkjQuSdKpOcfaJEnaL0nSSUmSfiNJUtmcx/5ZkqQuSZI6JUm6ac7xm5PHuiRJ\n+uic462SJB2QJOmCJEk/kyRJkzyuTf67K/l4S75+6TS/Z0k8KeOrONDp8IcxalWz3p6lYl2Vkd4p\nP+5kBZTdoCnJDndbgyVvucKRWOFT73JhW10ZZ4bdxAUcG3DRXG4s9ZDmMeBcvbP+VGTyzbkPuHnB\nse8AHxVCbAd+BXwIQJKkLcDfA1uTr/m6JElKSZKUwNeAVwBbgDcknwvwn8AXhRDrASdwd/L43YBT\nCLEO+GLyeQVlV5Ot0JdYxGq/Ixs1ypL7FdiS+aYOX5jdzTbiiLyVCmeKUiFxcSJ/qXPRVVg1t7vZ\nRueYZ15FX2SZPN1i8+czY6UeQlYsK7BCiCeBhbsdG4GZPsR/Bl6X/PlW4KdCiJAQogfoAvYm/+sS\nQnQLIcLAT4FbpYQv23XAL5Kvvx949Zxz3Z/8+RfAy6UC+rh94U+dPHSsuE5Gq5mN1Waayw2cG/Xw\nXLeD3SW4+cwwN4xyuM/JdAlyXrfWleEN5a+woRjFI9mwt9XO4T7nonLpVH4JpeTMiDuvN7pCk+u7\ndwp4VfLn1wONyZ/rgYE5zxtMHkt3vByYFkJEFxyfd67k467k8xchSdLbJEnqkCSpY2Iit2Z94Wi8\n6LMigNNDLtoaM6vOKTRVZi17W+2sqzLRM+Wjb8o/uwN/fNDJuqrSGCGPrQJHJb06v5ughlXmddqd\nRrRUitUlsELAG+55Dk+w+DfZXMj13Xsz8C5Jkg4DZmAmNyTVDFPkcHypcy0+KMQ9Qoh2IUR7ZWXl\nkgNPx1XrStMXKRqHzhFP3r/A2dLWYMETinKwx0HXuHe21HKGaBw8gQjGEgjDTF18KVn4fqyU1TQz\n3Ntiy/vvV0jGPSE+cYm4bOX0VxZCnBNC3CiE2A38BLiYfGiQ52ezAA3A8BLHJwGrJEmqBcfnnSv5\nuIXFoYq8sbvZhk5dmg99MBqn3lbadK3jgy4abPol34MxT4g1lcaibzBplFJWZuKFIN8J7qVqDZMK\ndzA6u4G4kNUXKU7w88ODPHoJxGNzUhRJkqqS/1cA/wp8M/nQw8DfJzMAWoH1wEHgELA+mTGgIbER\n9rBIlJE9BtyWfP0dwENzznVH8ufbgL+KApadRWPxkpaqrobNrgtjXrbVLx2uODnk5oqG4oY0onHB\nltrS+ZMaNUp68twWPLqKsgjK9KlTsZQKidFV3FXgQ784zpR3dXftzCRN6yfAfmCjJEmDkiTdTSIL\n4DxwjsSM83sAQojTwAPAGeAPwLuEELFkDPXdwB+Bs8ADyecCfAR4vyRJXSRirN9NHv8uUJ48/n5g\nNrWrEFgNGq7fXLr+9Ad7HEXrBaZRSmyqMbO20ohpwZI/k3vY0f5pdjcXb9Or1qLP2g81n2ytt+S1\nQgoS7ay31Zfe1Brg+ICTeqsOu3G+0O5ustLvWL0C6/RH+OKjKze1KSTLfmqFEG9I89CX0zz/08Cn\nUxz/PfD7FMe7SWQZLDweJLGBVjQ+dNNGHj07VjLjaXMRRGRXk5W+Kf+8DT29RkmDVY9Fr854+X9x\nwoterSy4ZeDeFjtHB5x57V6aDVc0WDjSl//IVCQuMGhWR6V6KCqotxlS9KJbPWGMdPy8Y5AP3LAR\n2ypYAaZi9UTaVwFrKk1sydBvsxAUUqx0KkVCLPqnF5nTBMIxLox76ehzciCDho+VJi0apaLg4lqm\nV3FyyFWyG15NmY5TQy4Ktf+zmpr6BVK5b61+fSUUjfPhB09ktPIqBbLALuC23Q0lq8F2FSi/06hR\n0lRuyJuZdkuFgfEV2PRlSjBPrlC5Um7SUGkqXKnwgCPAatnrStUexh24NFKhHu8cX7XtjmSBXcAb\nr2zmx2+9siTXDkZjeRV3tUJiZ6MVi16dVwNvqUhTm+3LbLjlC6tBzfoqE1VmLTsbrexrtdNk13N6\n2E1zReFKRVWK1eNFsblmcTz43KiH8lW69J5LJCZ4uiu3/PdCszqCQKuMva12ttaVcbrIPZ8mveEl\nG+Fly44ma0H6bLkCxbFZzNZUxaBRUmXWUmnWIoBTQ66UdoZrKoxYDWpUCgWT3hDdk77Z6rCFM/O+\nqcIZyuhUiZXFmZHiF7gsJJZiiV1j0aFcJTeA5fjN8RGu21S6Tep0yAKbhskSpX9M51G8+grUFfTC\nuBerQV3QktV6qw6DWklrhREhEl0hXGmWrHqNkk3VZo4NTNM75Z9d7qbztDVqlRzpX9xoMBVj7lDB\nGkJ6w7GSdyueIZDiZhaPC5orDPjC0ZKUJ2fDX8+NM+kN0ZesQOyb8nNxwsuYO8iQM8DD77maigKG\ne9IhC2wavvp/dvGp353leIqOn4Wkb8qPWavCE1p5y49xT4gynSptEnmuKBUSJq0KbzBKW2PCNzQm\nBPG44OjAdF46IVgNGo7Oee8loNyowWpQU27SMuUNcXHCR0u5gWjyugvpGvdQb9UvykDQZtgHa4ZD\nvc7Z/l/5ZjVszmyoNqXccBv3hBj3hGgpN6AAHAtEdl2lkZh4viptwOHHlwcrx1xwBSK0f+rRtI8/\ne3GKV7XVFXFECeQYbBr2tNjZUlt8w+lITLA5j9fdXIAE/UhMUGfVoVEpOJLMPOjodXKkf5o9LYsb\nOmbK2kojG2vM7GyyLgrPCGDKF+bihI+DPQ4uTvi4am054+4gg87Uu/EOf4RJb3CeG9iORmtOIZjD\nfU72tuQ397farKWUu1wapcTORgtj7tCSTSN7p/wolQpak/HotgYLG6vNdE346Jn0cW7UgyRRMnHN\nhNMlauQpC+wSrKsqjaN/KI9VPsFoYT70R/qcbKwxLyqlPNjj4IocNqe21pUx7g7ROerhaIbL965x\nL/5l2vyEooIj/dO0NVhYV2WiczS3uHo0LjjY62R3c/6sG+0mTYrc08W0NVpYV5n/zbYGm4GjA660\noZe5THhCeIIRttaVcXzQRefY/LixcZXk9Kbj3KinJKsFWWCXoLJEbv7HB1wr9iawGzW0N9vylpq1\nkGictEJ4atjFtrrMZ857W+2cHnZnHRYZ94SwpCnzXMiZYTeVRg2BLPqupSKf9qjLfd+rzVrWVho5\nPuAiEIlTU5a/z2OlSUtvlht4k95w2o3fjj4ndsPqzTh44vwEb/1+9s1QV4ossEuQMvm6SDSssN9U\nNB7nSL+zJJ1h4yKx+56pA1Y6q7xMKNNlNnOKxAX7exzsbbGvyCrw6MA066pMbKktY1dTIqVrT8v8\nfmGZUrbMBlckFp9tizM0HcBu1GDIkyFRMBJlU4rUrJVgWWXtZRbyeOcEn/n92aJeUxbYJXjNzoaS\n7DwCBFeYYO8ORLGW8APvCcUwapRLhhhNWiV7W+1MenPPnMjWuPpgrwN/OEaDVZ9zw8mucS9nRtwc\n6Z/mQI+DQ71ODvY62Neancgu9xqHPzLP8+HMiIfGcmNS4M3kmjK9t9WGWqXgzEh+0xAnPaGS2Flm\nSjQu+M5T3bz268/QlYeuwJkgC+wSaFQKNlSXxmR6wBFY0Uxre70Fh6+0qTVdE760nRCsBjVqpSKj\nGORStORYCDA4HaDOkl+LyFNDLvZkuRF2oMfBNRsq2DmnH1xrhZEttWbWV5mIxOLz/CE6Rz10jXs5\nP+ZdgQG6VJDPhicULZphUa7EBRzpn+ZjvzpZlOvJArsM779hQ0ma/jn84WUzADQqBVtqy9i6IN65\no9HKyRLtmi7k5ND07O7zXDZUmZfcuc6U00OujOOwC8l3w0lfODab0rUc+1rttDVY2N1k4+SQiz6H\nj/VVJirNWnomfZwZ8XBh3MuJQVdKG81oXFBdlp0Jeb1Vx+4mG6cK+NlYTT63S9E94SuKyfjq3vpb\nBbS32Dny8RvY9ck/F931PdVHVZM0bQlFYpwb9cwu83Y3W1FKCryhKN485NDmi1BU4AtFedEaOxPe\nMGU6FbG44NRwfr7kvnCMlnItsbjI+vcecQVZU2GkO89er65AhNYKI1VJsRz3hKg0aWkqNxCLCc6O\nuhhw+hmenm8inu2sctIbQqNSZPS5TGyQSRzuz3/BxAxVZi2H81SFWGgmvSG+8pcLvO/69aiUhZtA\nyQKbAXq1EpVCojgFos9zbsQ9zxKwudxAtVmXsjrpcN/zO/pmnYor6i1o1QqO9k/P9tUqFeOeEDUW\nXcHiXr1TfjbVmLNOdB90BlBKsL7KhFaloN/hz0tRhlGr4tyok55JH+3NNspNGs6PeZnwhtCqFMQF\ni8Q1F8bcQQwa5SKBVUiwodpMKBqnd9LHnlY7R/sXNzTMJ3qNkhqLrigmQPniq4910VJh5LbdDQW7\nhhwiyICO5MZIsdlYW0Zbg4V9rXbWVhrpm/KnFNeFeIJRTgy5ONTrpLpMS1uROxCk4sSgK++J+nM5\nN+phaw5WkzGRKP09NezOW1HG3CV9R5+TsyOe2WOhaDxvQufwRRZ9ga9osGDWqTk36qHCpGFdlYmD\nPY6Cu02tqTAWLCWwkDzTNVnQ88sz2GXom/Lx5vsOFfWaSoXE7mbbijeAAIamgwxNB9laZ2bMHVrR\njv1KOdjrzKuZzUJODk2zudbM2SzNU9TKhD9YvyM/oYKu8eKYt2iUEmadCn84xvpqEzq1cp5nQiH8\nE1LRYNMX3RgpGyx6NTsarTTY9Gyts9BkN1Bu0lCmVzNcYCN3WWCXYP/FKT7+0KmilQCqlRI7Gq0M\nOPx5Ede5nB72YNQoqTBpSiqyp4dd1Fp0jLjy38QwEInn1J13c20ZJwZdOW26lRs1VJVpZ0XdrFNh\n1KjQqJR5bzOzkE21ZSgliT5HgJNDpRM4i16dtly5FBg0Sv5mey237W6g3qanukyHOk2ctX6F+ebL\nIQtsGlz+CHd+72DWeZa5oJASO/+9k/6Czjp84RiVZi0WvXo2gb3YBCJxGnUqxtyJlJl8k0sTPF1S\nlFNZGy5FmU6FXq3k7IiH3U02eqd8TPnCeIJR1lYaCy6wOrUy5wyKfLGrycr5sdLbLc6lvcXOf7++\nrdTDAGSBTYkQgh8f7C+KuELerXWTAAAgAElEQVRCaIRIpGYVmt4pP/ta7SUTWIDzY172ttrzPkuH\nRMWTUaPMatURztGvYUONmY7kDXHu7nyxvIQnPCHsJSwmKdOpOD3sIhQt3SaqWimxodrMppoyWsoN\nbKwxL9sZuZjIApuCk0Mu/vMP54p6zQFnYbxbU5Hv/M9cONjjYGejdZHN4EwaZa5DjMZhV50lo83A\nGXIJV9gMajpTVEKplRKRIrXk7pn0YdYmdu9HCxByWY65N5hisb3ewivbarHo1Ri1Kq5eV4F1FXsg\nyAKbgp4850WmYm2lkXKjFmcgjIKEsBQrNnqo18mWWnPJnfQ7R93z/Frbm210T/hw+sPsbbETicU5\nP+aZNxvNZHbaOZb57PGKegsnMky831hjxh+OgkhsRKbqY6XXKPHm2X93KfyROC9eW86DR4aKdk1I\nfF49JejZ9bO3X7lquvFmwqUz0iIhhOD3J0cKcu4qs5aWciO9Uz4uTvhKukxPl7ZTU6ajwa4vyszE\nH4lTrZTQqBSsrzLNyy6YmYGurzZhM2jwBqNE43H6pnxoVYolwzeuQBSLXr2sDd++1tQdDxY+xxOM\nolEpuDjhxbOMeLoDUdZXmRku0oyya9xLTZmOlnJDSsEvFHtbCmNAPpeN1eZFtojeYPSSElg5D3YB\nB3oc/PH0WEHOLUikAq2GZGzlAqcQs07F3lY73mCEjl4nOxpTx7HsRjX6PBp6aFQK1lQYmEzznlwY\n83Kwx8GZETfnx7yEooKN1cvXuy9lhKJXK9hUY+ZAj2PJUER7i23W2OXYwPSy4jpDz6Qv5XtUqG7F\ncSF4+eaqnDIocqGl3FBwcb15aw3/83eLN6qy3YgsNbLALmCmqqcQTHhCqJUKNtWUxkBmLjNlpW0N\nFipNWsLRGAd7HETjAglSprVsqjHjD8Wos+i4osHCriYr9dbn6+GV0tLC1mDTs7fVxt5WGzNvsUqp\n4Nyol7EsbjqZeEMkzGRSDyYQiRNOEyc1apTsa7Wzq8nK2WE3UzlkAjh8YfQqBXtabOjm2AvesKUw\nTfme657i/mf7eN2uemqy9CfIhUI7zElSYqNwQ7V5kSfzuRwN00vFpTPXLhLlJi1f/T+7eMcPD6c0\n2VgpA84AFr161oZu1BVc1DOq0KgVErUWHfVWPYf7HMystrfWlTHqCqJWxTm+oCpnY7WZi+NeInEx\nL7Sxr9VOhUlLMBpnZDpAg82APxydt1wt06uw6NQMOAOz+ZLrq0z0O3xp8xOX4uKEF0laeiOsZ9KP\nWavEolemjG3b9BrsxsiiVKqtdZa8zM4c/giOXiftLTbOjXiwGdUFS2eKi8Qs9ocH+tlUY+a6TVUo\nFYnZ3s8PD+b9eoX8vJYbNfzyH6/CpFWhVipYV2liInnz1agU2C+BNuJzkQU2BddsqMSsUxWsk6Yr\nEJlnimHUKGkuN2LSKfGFYgVP8YnERcp825HpAGa9mr4pPyqFxJbaMsw6FXEhODfiJrLghrOzybpI\njM6MuLEa1FSatJh1KirMWpQS7O+e/7wL4152N1k5nGF7mLk4/RHWV5m4sIy3gScUY3OdhUlvCu+G\nficKKdFNYW6dftcKzL9T0dHr5CXrK3j24lRBbtgLOTfq4dxoQsjf9KLmvJ+/ukxbkCKRGexGDT2T\nPq7dWAXAnlY7+7unANhSW0b7Cnq+lQJZYFOgUSm4ZkMlDx0bLsr1fOHYPPPjJrth9m4djcUzbjG9\nUhz+CA5/hApTYpawnCGzO80m0rQ/wsYaM52jHronfdRbdexttROPC4ZdgVmjk1zEdQabQY1KIS1r\nZDPuTi8GcZFIF9vbauNgT+KGYzeq81ogoFLAlDdcFHFdyKgrSJVZm9eYf4VJy5i7MHsILeUG3rC3\niZ8dGuCaDZVIksSmmoSxeGuFccVtlEqBLLBpuHJNedEEdiH9jueX16Xwos0kXWy5NK+5zvYzfggA\nWpWE3ahZsYgd7HWyudbMhGdpf4XeKT+7m2yMeZ7vPltn1VFp0s6GQZz+CBKgUytmq7ryxfZ6a95n\nxZnypzNjvPUlrXz7qZ60z2lrtHLthkoqzFp6JnyMugM8eX4yrfWjUVs4yRhwBrhhSzVvvrp19tgr\nttVw7pOvKMn3IB/IApuGXxU5rzAdCikRM124PC81yxkrp4uthqKCSrMSWLnInh3xsK7SiCsQWdIt\naiYcUGfRMewKUmfRc2bETXuLjc5RD1a9mj2tdhTAc3neHXf6w6ytNHFsoDirkIWk+9hYDWo+99rt\n3LytdtFjLn+Et/+wg+e6F78XEwXMgInFBd968iKfevX22WOSJKFRXRom3qm4NG8LRUCxSt6Ztgbr\nqhNXSDhG2Y3pyzSXykE1aZV5m5F0Tfhoa7AuWZOvVkrERcIjdU2lkY4+J/5wjI5eJ0aNihFXkIM9\njoKURvdO+Wm2G3jrS1p57c76nPto5YJWpaDSpOHv2hvnHb9hSzUPvvOqlOIKieaFP7h7H//7hp2z\n4SJIvI+9BS7CGVpFpjH5QJ7BpsEXKr7/60IMGuWiROvVQigqWFelT+vCH4rGk2Wji28Og44Apjya\nlHT0OWmy66m16GY3eGZQSAnHJE8wyuPnJxa9dnROjPbowDQbqk2cH8vvkv63J0eIxQUqhcQ/7Gvi\nD6fHCjoT1KkV/OO163jHNWtnb2T/cetWeiZ9lOnV8xyk4nHBr48NcaTfyZ4WO69qq0OSJNRKBa9s\nq+OGLdX89dw4h3odTPsjdE/68AYjBSuSGXOHCIRjec21LiWywKahmDONdGyrtxTEECVfLNWUsWfS\nl9bQxRuO0VRuzGv9fL8jgEmb6GKrUSaaVWpUSoKRKJUmXUpxTcWYO4TNoM5Lv7AZZja4onGBSavm\n79ob+O7TvbOdKvLNg++8apH5uE6tnGcoPuEJ8aczozx6ZozHOhPvzQ+f6+dnhwb491dtZUOymEOn\nVnLL9lpu2f78bDccjfPtp7r5xuMX896e6MyIm9PDrksuWyAdssCmYMQVKHnTwO31ZRxaxeIKEF3G\nJf/U4DQmrSrll7Br3ENbg4VQNL5o1pkr3lCMfa12HP7wPH/Uva2Zz5ZdgQg7Gq04/YWJmZr1Ksbd\noYKJ601bq2fFdXg6wKkhFwd7HLgCEYxaFe5AhNPD7tmV0a4m67zXP3txipu+9CSv393Ah27atCjR\nHxIbr+962Tpu293Afc/28q0nLuZsPVlv1XPlmnIEgs01ZexqtrK7+fIQV8hAYCVJuhf4W2BcCLEt\neWwH8E1AB0SBfxRCHJQSOx9fBm4B/MCdQogjydfcAfxr8rSfEkLcnzy+G7gP0AO/B94rhBCSJNmB\nnwEtQC9wuxCiKNY9oUi8IF6lmbIvOfNbfZHX+XSOuik3atJWO/kjcdqbLSk7GIRjguOD2be5Xo6F\nebkbq83sarJxRb0F5Uxll0jMlBbmppp1qkQvq2XEr8Kk4ZbttZh1Kk4Oudl/cZJYXGT0mfnqX7vY\nUlvGnVe1cN+zvdn+ektSZdbyhdt3ABCJxXn7Dw4vO1FQpFiqCQEPdAzy8PFhXrOznu31VgSCRpuB\ntVWm2RBDdZmOj9y8iU01Zt7702M5jfnajZV8+jXbl3/iJYoklvGFkyTppYAX+P4cgf0T8EUhxCOS\nJN0CfFgIcW3y5/eQENh9wJeFEPuSYtkBtJMoyT8M7BZCOCVJOgi8F3iOhMB+JXne/wIcQojPSZL0\nUcAmhPjIcr9Qe3u76OjoyOW9mMcnfnOGe59Jn95SKDJJoF9N7GmxLWkSvqHahCcYXTI5fW+rHV8o\nmtcCC0mC/3rdFbxuV32ygszPkDNIMBpDp1JiN2rYVl/GmRE3A44AE54QAw4/Dx1fOjXv1Tvq+M/b\nruDiuI9zo27WVpqotej49O/P8vDx4bTVZeurTHz2tdvZ2WQjGInx6NmxnEUpHe+7fj3vu34DAF97\nrIv//mPnsq/J1pdXkhJGL5973RXz2rHf+tWnF1X/ZYJZq6Lj49ejVV1aMVdJkg4LIdqXe96yM1gh\nxJOSJLUsPAzMBHQswMyn8lYSQiyA5yRJskqSVAtcC/xZCOFIDu7PwM2SJD0OlAkh9iePfx94NfBI\n8lzXJs97P/A4sKzA5osme2mSmi+1UsDlqnrOj3kxaZQYNMq0jSMP9jjYVLO8gUs23LSlhpu31fCG\nbx/gQI+DWosOdyAyz+rQZkj0amqyG9hcW8abrmrm3det40cH+vnxwf5F3VrvvKqFf/vbLfzpzCjv\n+OGR2eMfvHEDn399G8cGpulLZWGoVvLtN7VjM2j48YE+btpWw6076pn0hvnkb8/k5fct06n4uz2J\nbIFAOMaDRzIrkc12q0GIxCrhnT88zO/+6SWzpkGv2lGfk8B6QlEeOjbM7QsyHS4Xcs2VeR/w35Ik\nDQCfB/45ebweGJjzvMHksaWOD6Y4DlAthBgBSP6/Ksex5sQdV7XwudcWf+niCRbfY3MlxOLxRc5c\nC1lbZVq2K+/5MQ/mPCaxv7Ktju/v75sNGYy4grTMmXFBosDgsc4J7t/fx0d/eZIXffavfPfpHm5v\nb+SJD13Le1++nuZyAwB3vKiZ//fKLfz25Aj/9JP5M89fHhlCrVRwYxozlzfsbaKlwsi7fnyEjz90\nmjffd4hgJMZdV7Vw/eb8fKzfe/0Gai2JScH39/fSneEuf67G5udGPXz7qe7Zf9+2u4EmuyHr89iN\nGmothTeoKRW5fqLfCfx/QogHJUm6HfgucD2pb4gih+NZIUnS24C3ATQ1NWX78nTn5O/3NnGwx8Ev\njxan6GBDtYkLeU4RKhTrq0wYtEq0SgUjrqVTjjL5g8YFrKs2cTRPZcGNdj0PH5//d1tOTGJxwU8P\nDfC7EyO8+7rEJs77rl9PKBrHFYjwsV+d5GeHBhbFWmdyftN5V7Qn48wzpcenhtx84rdn+MxrtvOF\n23fw1vs75vnSShKsrTSxpsJImV5NXAicvjADzgDdE97Z66sUEuuqTLxmZz3/sC/xuR91BfnKXy5k\n9B7ByrpbfP2xLu56cQtaVaI32C3ba/nmExeXfd2eFhv/98pmrl5XQSwuqCqCA1ipyFVg7yARNwX4\nOfCd5M+DwNy5fgOJ8MEgzy/3Z44/njzekOL5AGOSJNUKIUaSYYbxdIMRQtwD3AOJGGz2v056yorQ\nVE6nVnBFvTWrNieFpkynwr2E/6ndqMnYderEoCujdtqBcJTt9ZaUGzPb6y3o1IqMm0KWm+bX4Nda\ndBl3qvBHYnz2kXN89pFz1Fp0hKLxJavOzLrE1yhdcdvZETe3bK+lwaafPc+PD/Szq8nGbbsb+Mnb\nruTUkItjA9M02PS0N9uxpOm15Q9HuTDmpUyvptGmRzWnYu7koIt3/PBwVv3IViKw7mCU3kk/G5Ph\nnUyKR/a02Pj5O67K+ZqXGrkK7DBwDQmRvA6YuWU+DLxbkqSfktjkciUF8o/AZyRJmtkyvhH4ZyGE\nQ5IkjyRJVwIHgDcB/zvnXHcAn0v+/6Ecx7oi/nh6tKDnby434PCGSiqu9TY99VY90VgcTzCKSafC\nH4riDqafTU/7w5i1KjwZ5kHG4yJt4cEM50a9qBSJHOS4SMQuLQY11WVaLo57aS43pn3tQmwGNZNz\nOsw22Q2MuDJ7j+dmFmTiHDXjX6BMU/730LFh3nf9Bva22DkxJ075oV8cx2ZQ8/LN1bQ1WmlrnJ8y\n5Q1FGZ4OYNKqMOlUmLUqDBrV7PMC4RgXJzycHHLxeOc4fzk7nnX610r7hwXnXC+dSftcLqcUrEzI\nJE3rJyRmnxWSJA0C/w94K/BlSZJUQJDk8pxEFsAtQBeJNK27AJJC+kngUPJ5n5jZ8CIRbriPRJrW\nI8n/ICGsD0iSdDfQD7w+598yR/qmfAWvuDFpVSk3RorF3hY7h/scKUsUdzRa09bQm3VqttRpODPi\nRqmQlrV27BzzZtRtNRpP5GYGIjF6JnyMuYKzBQmnh91sqyvj1DLnMGqU6NVKJj2J2aJSoqDv8Uye\nry/Nzabf4efYwDTvetk6Hj8/QVcyS0QIuPv+DnY1WbluUxVb6yzE4oJjA9M83TXJqSHXPLcwjUpB\npUmLSatizBPEHYisOJ1wqRteJszthLy72b5kOx+FBC9ZX7Gi611qZJJF8IY0D+1O8VwBvCvNee4F\n7k1xvAPYluL4FPDy5cZXKFz+CG+5v2NZO7xcsRnUBMNRNEpFRsvnhdiNGtZVmnAFIsSFICZExhsb\nc4kJQbrv2InBafa2pO5bNekNMeEJoVUpaS43cDSDxPzTw272tNg4NjC95Bd7KXvGTPoxWfQJy8GZ\n2VxMQLlJg0mnmhW3fFBt1uILR6kp0xGKxnj55ioeTpPm9fk/dvKTt13Jq3fU8fk/nZ/32JH+6Yws\nKcPReN7Nrlc6gx2eMx6LXs0nbt3KRx48CSQq6q7dWMmVa8oTrbVrzQXvhrDakCu50uCPRAuaj1pu\n0tI1HpltW11TpqW53Mj5Mc+8Ms1UM7ZdTVZ6J32LhG9noxWlQiIWF2hUCgKRGFqVAkmSOD3kYkO1\nefZ6rRWGWW/PMr0Kd2Dx7Csu4HCfgy21ZQw6/dRZ9ZTp1AjErEj6wjFqstgFnuloe27Uk9vsK4O8\nIqtBs+hvNzNzzkdzQJUiMdNuLjdwqM9JR5+T3xwf4bpNVejUipR9o2Y2uFJVRpWSTPuMpeO3x0e4\nvb1x1j3t7/Y00WAzsL7KRJlenXf7x0sNWWDToFEq0KgUi3Ih88VC96dRd4hRdwhFsh+RQaNkwhPi\n1LB73iyy0abnzLCbYIpxHV3CEs+gVnB0YJpt9WWEo3ECkVhGG0Yx8bw4uNOUtPrC2X1Jz4x42Nlo\nXXK8aclAlG/aWsP+i1MpH1vpF35fq53Tw270GiUH57x/X/7Lef72ilo+8aptfPjBE4te9/JNiXSs\nbFcqhWbKG1q2S+9S7O+e4heHB3nD3uezd1687oUVBlgKWWBTIITgO0/3FExcIX2H0bhgUZzy5NA0\nVoOaMp0ahzeUUlyXw5+cVV0Y8xCNpQ8L5EIsLrh6XQW7mqzoNEoePja8rL/A8HQgo9baC3EHI0ve\n+Oqtet7yklZe+dWnFz2Ws6gnWVNhZMwdxBuKLvJXGHAE+MiDJ/j869tosOn59lPddI56qLHouG5T\nFW95yRoePTPGjw705Xz9QhATUGFQr6hLwb/++hQ1ZTpetmnpnN5wNM6vjg5y6476F8zMVhbYBQgh\nePXXn+V4gQ2Ss0mPCUTiWPRqxtwBQtGVKeNKX78Qk1bFv79yC/2OAL84PIheo+R916/nifOT/ORg\nf9rXjXlCbKktw6RTZeUBem7Uw5ZaMwPOwKLlrdWg5jt3tPNAx0DKePTYEu1jMkGtlOgcSx/nfujY\nMBLw1peu4Xt37Z09PukN8Y3HL/KNxy+ueFOpEKy03XcsLrjrvkO8ekcdb3xRC1c0WGZDBvG44HC/\nk6cvTPLD5/qY8oW558lufvuel1w2loRLIQvsAr7++MWCiyuwbGXTQkYL1AcJErvu//iydayvNuHw\nhhl0+vnBc/0ZzS7vu6udxzon+NpjzyeYH+l3ct9de3jk1MiS2QVnRtyYtEpqLbqM0qGqy7Tsay3n\n6nUVbG8ow6xT0z3h48yIm2a7gbZGC995uoenzk+mfH2D3cDwCiwSvRl4BB/pn+ZvvvI0779hA4NO\nP71T/lVtOQmJuPBK49IAvz42zK+PDWPSqlhTaUSlkHD4wkx6w/Nm/FqV8gUhriAL7DwcvjBfe6yr\n4Neps+joXCX93ddWGvnG/93Nlx49z+f/1IkQiaT+b71xFx944MSSu9Z7W+wc7XfNE1dIpB/9YH8f\nd17VwpceXbqqyBuKUW81MO4JLWoMWKZTcePWGq5aW057sx0QPNfj4EC3g68/3kVcwKYaM2sqTfRM\nTPD+B44Tjceps6T2keie8Ka1T8yETBLpy00a3MEInaNunu6ayjoEUkzqrTqqzLpl2/9kizcUnZfv\nO/+aeu68qiWv11vNyAI7B6UkrThtJRNWOpPKFxUmDbdsq+Gzvz87a7oMcHLIxTt/eITPvGY77/zR\nkbSvf/G6Cr71ZOrSyLMjHt593fplBRagc8zDvlb7bGWYQoLPvGY712+uZn/3FH84PcoX/nQ+pdgn\nGkSOzf67rdHC8YHUX+7WCuOKfH7tBg09LJ0KN1Pq6wvHVqW4mrRKwtE44ZjAatBwdGC6qOby77hm\nDbfvuTyNXVIhC+wcvvDnzqLEyKa8hVvuZ0M4GicSF/PEdQanP8KPDvTPE76FaJIpYKkYcwexpSn3\nTMWBHge7mqwc6Z/m2o1VvLKtjtd8/Zms2rc02vVpxRUSKWI7G60MOP0Zdc5dyOH+zMp0r6i3cG4V\nZQtsqDYRjMQpN2m4MOZlW70FpUKazSIplvexUaNcVK12ubNKWvuVnklviB8fSL8pk0+iRZglZ8IH\nbtzAd5Zo6fzsxUluSOMQBYnKJWUagY3GRdbWi6eHXVy/uYrX7a7nV0eHsu6NVZuBacjRgWnWVpqy\nOu9c1Mrlp3sGrXJer690aFUKWiuMS7beWQkqBbQ325jwhOh3+DnaP403FOVI/zSHU5igF5qXrK/k\nioYXlsDKM9gkfzk7VrCqrYXUWPT0OUrbPXNrnZnfnRxd8neOi0TzwkqzNmXJ8KYaM9OB9DNBhy/M\nuipTxtVToajg0bPjvPnqVn51ZGnj61R0Z2jmsjDWmw06tZJIbOkY7omBaSx6NeVGDZVmLeFoDLVK\nSTQWT3QQEIkber/Dj1opZb3hmQl2g5qWCmPKbhJQvFnrXP5wepSHjw/zqra64l+8RMgz2CQ9k8Xz\nAyhUP6ZM2d1s41Vt9Rntbn/vmR4+/eptaJTzPypVZi2eUDRl1dIMX/1rF3df3ZrV2CQJNtWU8Vx3\n6kKBVNgManY1WTNe9p8cmqbRps/JvzST1/gjcdZXmRAIDvQ4ODqQ6It1pH+ajt5E5VfvlJ+4SHg6\nlOfZZN2oUaJQSBmV3xYbu+HSMpRfKbLAJhlwFk9gderSvO1Wg5oP3LCBKrOWzz5yLqPXTHrDfOWv\nF/if29vYVl+GXq2krcHCB2/cyOfSnGNzrZn2FhuH+53UmLW8Zmd9yuelYl+rnVFXMKOdfqVCor3Z\nhj8czUpMQlFBIBIjHhdoM8gMmEum6UUdfU4qTFo0y4QUBhz+tD3NcmVbvSWnGHMxMGhfGOlZM8gh\ngiQXi9gH6/iAiwabnsEsEuxXyp1XtaDXKPnmExez8guFhEH0h35xgpu2VnPV2gq6J7x89Jcn0i4z\nQ9E4Z0cSS9N3/vgI379rL5PeEE9dSJ2fOpfb2xv5Xga90FrKDSgkKe0SeDlmBCjbnlTT/gh2o2ZJ\nf9gZlitF3tWU3q1sJRQi5JAP1lWZsMkz2BceAw5/3lpHZ0IoGl/kRVBo3nHNGr77VE/W4jpDIBLj\n18eGuefJbh49O75kDC80J2wQjMR5y/c7eMvVrdzzxt28dENl2o2iG7dUs6mmjF8fW76DRJlOnXHM\ndSmybdHTNe7FrFVSplv53KR70leQWGgmG3HFZk+LjQffcdW8RokvBF7wM1hPMMK//vpU0a5nN6pZ\nV2laUU18LvRM+gjnOXuhyqwlGotj1qmIxAVjriAxwaLruINR7rzvEG/Y28Snbt2GWafityeG6Rr3\nEo0LtCol12+pIhiO8aZ7D2aUKperOclCjBnYHy6kzxFYtptuJtSU6Zb10c2F1VSMW2/VE4rG+PRr\ntqft0nA584IXWFcgQoVJy5f/fgfnRj38vGNwnhN+vlApJHY12zg77JrnwlQM9rTYONznRJJyb3K3\nkN3NttlUH0dSJJQKiXqrDkeK+J8QiTYpPz3Yz0vWV7JvjZ1dzTbUSgVnR9z81x86s1ouWwz5+ehm\n2pFhIfmoftIWIBa/o8Fa1NXYcswUhxTiRnIp8IIX2AabgS/c3gYk+oS/5epWXvm/T+e10qrepken\nUpSkJl2jlDg97KZn0sc7rlnLPU92ryhNCUCrkuhM8SWOxcWyxi1xAU+cn+CJ84nihjqLjklfOGvn\nMm9w5XHGfMxCV4IizyWqAMcGpzFqlLNFG6uBBpse6wtw9gpyDHYR5SYtn399Gy/dUJmX87U325jy\nhLiYQ7eBfNBUbsAfjjHpDfOLw4O885q1WRcALMSi1+Rcz7+QYVcwp/LkmRzSXDGoFStqIxOOxFdc\nIHC0f5qdTVa21Jat6DwLyTXOnm/2tNj4m+213HfXXjZUm0s9nJIgiXytGVcJ7e3toqOjY8XnEUJw\nashN14QHvVqJWafmk789k9Xyq8luSNbKFwaDOmGUPDdkObfvlQSYdCrC0fhszFKjVHDni1uQgG89\n2b34pBmQSW+tYpBNEcNc9GoFjXZD1pViC8k2AyEdayuNeb8Bry9xC/jNtWX8/p+uzruRzGpBkqTD\nQoj25Z73gg8RpEOSJLY3WNje8HynzP99w04+9buznBt14/CFl92MqSnT5U1gt9SWoVZKaNUJs46Z\n/Mk6q45KsxatUploQDjnA721vgynPzJv2R6OxbnnyW7WV5m4dmMlj6fwIVgOo3Z1fGxMOY5jW71l\nxaEBg1rBhbH8xDqHpwN5F9lxd3C2fVCxuWV7DR+7ZfNlK67ZsDq+KZcI66vN3P/mhJFyMBKj3+Hn\ne8/08IvDg/PEdl2VCYNGyag7P3muS3VSHZ4OMjz9fLz4xJCLTTVmgpEYp4bSzzIvjHtpsOlRKaSs\nS4QvjHlWxSw2Fhc02w30ZXkTy0Vz7AYNURHHHYiytc6MQaPKW/x2Q7UZrVrBuCe04h5ZM7gCUTZU\nm1Y8S8+FV++op8GWfZXc5YgssDmiUyvZUG2mpdyIRCLe5A5GGXT4MWuVHF3C1SkbKk1aBrPsJJpp\nGMPpj7Cm0pj1l9Dpj+D0R9jXaudQr6Mkde2QsFVstKf2fl2Kw31O1leZMm5qWVOmRalU4A8J1lQY\nOT2c313644MuFFLixqnJL40AACAASURBVOwJ5kcQtSopZSPLQrKh2sTHbtnMtRuXbh3zQkIW2BUw\n7Q/zxPkJwjExbzaTL3FVSAkD50Kk3ejUCl67q57/+M2ZnM9xoMfBphoTQ9PBvM28ssWcY5hgyhem\nwqTJqKS01qqf9Xl1FijdSKtSzN7otCoF4Vg855Q6rUrCqFUXJN1wIRUmLR//280A3LK9drZVjEwC\n+d1YAROeEM+m6V66UtoaLWyoNhdEXNsaLPzr32zhO0/1rDhGd27Uy4aq0u0QT+VYc+/whTO2LRwo\n4EblDApJoi7Z/nx7vYUqk5ZNNbm9ryqFIqfQTy68/aVruHVHPbfuqJfFNQXyDHYFNNoNPPmhl3H9\nF5/IewfaYDhOZx42UVQKif+zr4nWCiPnxzzo1SoujHv4+EOn8lZ0cLjfWbKYbFO5gbEUVoqZMJ7B\n65RScWr7feEYvnCMRrseQaIp5JgnxI5GK52jnowd2GZCDcfTtGzJN7fueOFYD+aCLLArQKdW0lRu\nwKxVMRXNr3tR55iHnU1WnL7wihrSffjmjQB8+S8XClpNk02X3HwyNB1gX6sdgMN9DrK5z/VM+thS\nW8aZkfQ3Bp1aWdTSU51KybT/+c/SsYHprAoi2hqts+GMQnP95mqqMjA5fyEjC2weMGpVebecg0Qi\nut2gSba3VgISR/udGbe1uaLBwu9OjBR8NlNn1eXUaseiV7O+ykQoGkOrUubkjDWTRbG9viwrcZ3B\nuIx9ni8cK3g+81zMOtUil7VDvU6uqLdwYpl+YlfUW1K2Ky8U12yoKNq1LlVkgc0D/7CvKWN/1Wxx\n+MM4kjMag1rB5pqyZb9oM6yrMvHLI8s7U62EPS02Tg655qWKATTa9FSatbO5mOFYnL5JP/U2PWV6\nNe5AhM5Rz6yobqjOvY2LQoJAjsv4syNuFFL61K16q66oXsFH+qfZ22JfFL4YcQUp06lwL7GZ6A5G\nitpoMV+GO5czssDmgZk66zqLDptRU5BY5NpKI4FILGNxBbgw5qW92ZazZ+pSVJdpaa0w8lz3/Eqm\nTTVmhBB0jnkZSOFLkG7TbiX7MXFBzisIbygR9xxI08KnzqpnaLq4HYC7Jrxsry9Dp1ZyYdzLtD/C\nhDfE+moT0XggZUxYkqDZbkCrVqb0icgXH33FJn7eMUDvlJ/dzbaCXedyQRbYPLCh2syD77yKnY1W\nPvrLE3kX2LZGC2eG3Vkvw08Oubh5a01exwKwo9HKxQnvvAyEzbVmFJKU8+++Un8Ek1aFxPPOXtlQ\nadIuEti2BguxuCiJGYzDF5419F5XaZyNnV8Y87K7ycrhFDHWtgYrTyQNzavNWlRKRco25ythTYWR\nt1zdyttesoah6QCNObTceaEh51XkgZ1NNnY321AopEVL5XygUylzbieez80nvUbJ7qQLvycY5VCv\nk6vWltNaYeTsiGdFN5ZYLM6uJittc0qTs2HUHaTJnpuZs1Ixv6RTr1bQPeFLWz1XKPQaJXtb7ajm\njKdsgTH74f5pNi4wTtlaV8bpOSubMU8ImzH/7lU3batBpVSgUEiyuGaILLB55odv2UfXp1/B+2/Y\nsOJzqRUSu5qsHFiBocjBXgd/v6dx3rFGm562BguVZm3GnRXsRg11Ft282dOuJivPXpyiJw+dBQ73\nT3OkfxqdOjeHql1NNo4N5rZ7fnLQxY7G54XdoFHhDRevcGJTjZn2ZhtVJg0Hexxsb7DMiqwqRW7p\ntD+MPSmgW2rLOD/mIbIgxnJqyE17npfwsrNA9sgCWwBUSgWaBc30Kkxa/uaKWp740LX8w74mFBl8\nWrfVW1bs6Tntj/CnM2O8YlsNbY2JnvRGrYruCS/xuMAfjs6mOZUbNSktAC16NVqVYpEZSb5zfyF3\nN36xgmSqYDRO35R/tgGiLxzlRWvKaW+xUWPRLVstVmvRUWXWsq/VnrWF4aYaMxcnvHT0OWdbuR/t\nn2ZtpTFx3RS/1pgnRDQuuKLBQr/Dl3Z1c3xw8Wx3Jaw0jPNCRI7BFog3XtlMhUnLn06P4vCF+Y9b\nt7K1LjFL+vRrtrOrycYHfn487evTxdpyweEL88ipUeD5fk2eUAxCic2SAz0O9rXaCYRjNCoMizoL\nNJcbOJEi1SvbjqyZ4PJHcqpCWmkkxOmPzBZLbKuzLKrQ29dq5+yImw3VZjr6nGhUChqSRurdEz6C\n0TjjnhDNdj06tSqjIpE9LTZODblSCmTnmJfWCiOHelOvXtyBaMq/yVwiMcGAw8fGavOKi1YUEvKm\nVg7IAlsgjFoVt+1u4LbdDSkfv2V7LR19Tn5ysD/NGQqzIIvEBJ1jHtpbbHTM2cA50ONAIrHUnstS\nSe5LpQzlSueYhyqzllqLDkmSOD44nZF4LmeNt6fFRjQu0CSX3IKE569CkjjY60AI6J30sanGjDe0\neKNsJkzT0efEpFESisVT5pz2OQKYdSpaKwz0TKZP79pUY6aj17nkvDsfoRd/JE7PpJedTSsrQJAk\nifue7f3/2zvzOKmqK/F/T21dvS/03tDQQLN0NyDQgBJxBVwjJmpETYLRxAlqzIyT+UVDZtSY/Caa\nySQzYxLjb2RinCToz2jGn4njuKGTiWFTQHYaaKShoYFe6X25vz/qdlPdXUtXdVVXddX9fj716ffO\nu+++U9WvTt137rnnML/YGNlA8DsEEZH1IlInIrvcZC+IyHb9qhaR7W7HHhaRKhHZLyJXucmv1rIq\nEXnITV4iIptE5KDu16HlCXq/Sh+fEqo3HQ0kOqw8ekMZ2SkJHo9v+6SBwozwrJJRCnYcayQ/bfC1\nFXDgVPPApM/UnGS2+/hStnX1hOWxsa6lkx01TWw/1sis/FSK9YRKUWYi03OTmTcxnaH2tMdHVYTF\nJVlsqW7go08a2XSknk1H6tl8pJ4t1Q1sOlJPeWEaaU4b+elO0hPt7DvpO6PVua5en5OOLR09ZCV7\n/r+CqxBgqtM2ZivEunoVH+n4WttIfFMeSLJb+afV80OsWewzkhHsL4CngF/2C5RSt/Zvi8gPgSa9\nXQasBsqBQuAtEemf7fkJsAKoAbaIyKtKqT3AE8CPlFIbRORp4G7gZ/pvg1Jquois1u0GrhsLJNis\nPHnzHF7bUcvVFfnk6gTdG/fVsXF/HUUZiWGJSgDXSLY4K5mTzYMD2ls6e5lTlI5Tz6QPnTxx53hj\nB5VTMgdCisLB3lrXo21hhhObCFV1rlFdbmoCkzITOXymlfREO0e8rGBaNCXTb9WB/ry5zR09ZCaF\n5gfjrJdMVrMLUqk6dS7kIVQjYXN1PTPzUuhTDEvVWFGUhtPm8h/39KlhbqLZhaEtaxMv+DWwSqn3\nvY0exfVc9jngCi1aBWxQSnUCR0SkClisj1UppQ7r8zYAq0Rkrz73dt3mOeBRXAZ2ld4GeAl4SkRE\nxViNmytm5XHFrLyB/QsmZXDdnAJ++m4VL38U3lVY3X2eR30fB7CYoSfI8LFAGfpDU9fSSV1LJ1nJ\ndmwWocHDCqasZEdAlWrB+2cSKCebOlhQnEFHdy97al15JVQfQUc6hIr9OiViUYaT3FQnXb19nG7p\nHJacvawglYa2bmp18c/irCSO1beZ8KwAGe0sxTLglFLqoN4vAo65Ha/RMm/yCUCjUqpniHxQX/p4\nk24f81gtwrIZOSHxwfniTJBZqNzx9Wg+FkzNTqHKw+hVxDW7H2j88I5jTcydmB50OZp+Onr6+PCT\nRk40dTApy5VPNtLG1Z3jjR18dKyR3SeaPWYV21PbwqnmDlf4WGoCL22r4Xu/3xsBTcc3ozWwtwG/\ncdv35OBRQch99TUMEblHRLaKyNbTpwOvMRWNVBSmjSiUK1jSE+0el7IGSkNb+NwD/kh2WGnvdoWZ\n5Whf9qz8VIozk1hYnBH0woedNU1B52IdSmNbt9dluNFOn3JN6tW1dHLvZdN4/MaKSKs07gjawIqI\nDfgs8IKbuAZwj2qfCJzwIT8DZOi+3OWD+tLH0wGPzjSl1DNKqUqlVGVOTmjKbUeSju5eXv7wOAm2\n0ZWF9sVoR2j9HG/sCKpsSyho7epl94kWNh2pp76ti/LCNA6dPkdqoo2tR0c3WrSYgn2D+OnGQzz1\nzkH6IlUfaJwymhHscmCfUqrGTfYqsFpHAJQApcBmYAtQqiMGHLgmwl7V/tR3gZv1+WuA/3Dra43e\nvhl4J9b8r96wWoRvvfKx1yTL+WlObq2cxFXleUGPcp320MWwFqZHxsC609un2H2imdLcVA4FUcp7\nKFV1LWSnmMB6d5774CgvfVjjv6FhAL/DGBH5DXAZkC0iNcAjSqlncRlJd/cASqndIvIisAfoAe5T\nSvXqfu4H3gCswHql1G592jeBDSLyXeAj4FktfxZ4Xk+U1evrxQV2q4X7r5hOTUM7L207f0M7bBbu\nu2w6ay+bNrBS7M09p7jn+a0BB9oHuyTVE5EoDe0Nu1XoCMEKs/q2bsoKUkdUsyue+PGbB7hpwcRh\n+RsMnpFYGxRWVlaqrVu3RlqNkPH4a3s4cqaVhZMzuboi32MdqT8ePMPaX20LqPDgrPzQ1vsqyU4O\n+6TcSEhJsNHe1UOoght8lUyPV97+60tHXM8sVhGRbUqpSn/tTC6CKOdvry9j/Z2LuO/y6V5v6otL\ns3n1/ou5cGrWiPsNdbLkaFmnfq6zhwtCuNpo14lm5k/KoCgzEafdQuXkTNKc8b0A8mCAZd7jGWNg\nY4SS7GR+8aXF/OXyUlJHYABaOkKb+b47wuFa7mw72sCiKaEzsh8dayTRbmVCcgJbjzYwM0QRBuMV\n78u7DUMxBjaGcNqt/OXyGfzDLfP8tk1zhi5faG5qgt/EI2PNkTOtpPiptxUIVXXnV1/5y3sQi7gv\nsX3vwGn+8HFtBLUZP8T3s06MsrIsj0tn5PDeAc8xwSIEnFbPF2mJ9hGVwB5LclOdPqvFBovDKuwL\nQ7/RztevLGXtZdNo6+6lo7uXjMTocAlFO2YEG4OICP9823xmF3hePz5/UkZIJ26Onm0NW2KaYMhJ\nTaC2KTzB/V29itzUhJC6IMYDW482YLNaSHPayU11Dst3bPCM+ZRilPREO+vvrByWs7UkO5k9IZ4V\n7+5VWBDmFIU2IciconQWTs70mATcG7mpCUxIdtAQRG2ukVJ1upUt1aH180Y7je3dxFrE0VhgDGwM\nU5CeyN9cNXNg/4JJGawoyw1JnOhQahrbOdcZXOlsT2SnOKg+28q2ow3Myk8dZmSTHFZKc1NYXJJF\n5eRMSrKTWFySxbSc5JCGn/liS3UDZQWpLCnJCuhHYDyy41ijx2q2Bt8YH2yMc/fFJZTmpTIh2UFF\nUTo9vX28tbfOY7Lo0XLkTCtLSrJQ4DdFoC/y0hKwWywDQf4fH29mfnEGTW3dZKU4qG1s53hjx7CU\ne0fOtGG3ukbSHx8fGz/pntoW7BahJw5Gd/WtXSSHaIl1vGA+rRhHRLh0xvn8DLtPNDMh2REWAwuu\n5Ca1ze2kJ9pp8pBCMD3RTsmEZE62dGC1CMcb2qmcnIlCsbe2hVn5qeytbaa9e/Aouz8b/2E/ixm6\ne9WYz/LnpTupCUHinGgnDn5DQo5xEcQZswvSePEvLmJ+cUbI+06wCX1K0dzeQ1qizeNa/tLcFLbX\nNHKyqYPunj7mT8pg69EGth1txGmz8uEnjcOMa6CcbApPknJvJIZw2XE087vt4c1PHIsYAxtnOGwW\nRGRY7a3RkpJgpaIoY+Cx/Vh9O1YRytwiGaZMSGLb0fP1vepaOvnILSF2fYhSH471OvlwRSxEGz9+\n6wCtnWNXzjwWMAY2Trl+bkFI+5uWkzLIeIKrvPSe2uaBpM0iMiZ1qGqbOpg3KX0MruTiXGcvkye4\nMv3PKUqjIsTRFNHC8tl5xgcbIMbAxikVRekUpgcXu5qTmjCwHLcw3cljN5SzZKr3YhNbjzZwuqVz\nTJPB9FePHSvsViE/LYFdx5tJdsSmEbpxfpH/RoZBxOadYPCL3WrhxvlF/HTjoYDPfeCK6Xz+wsl0\ndPfhtLtcDv/634d9njPW8yOnmsd2ZVl/MUZwlf+ORU41j61vOxYwI9g4JpjJoBVledyxZDIiQqLD\nOjBjXxAFSbfdae/upTQ3Min1TrV0MjUnOSLXDhcWgeNxECkRaswINs5QSrH+f6p5e+8pNgUQq2q1\nCF9eVsLfrJyJxcMkUl5aQijVHDWnWzopiuDy3aQYiyxYPjuPb19fFmk1xh3GwMYZb+w+yeOv7Qno\nHLtVeOmrS5k3yXtol32MfZ4j4ciZtohdO5QVI6KBxSUjzzVsOE/0fSsMYaU/YD8QHl9V4dO4AiSE\nsMZXqGhq745YEppYymiY5rTxhYsmR1qNcUn0fSsMYaUxgCQoKQk2fnzrBdxSOclv26a2blKdNq4q\nzws6OiHUJDmsWCNk6bZUN7A4RpLBTMpKCmuF41jGuAjijJbOkRlYh83C92+aw/VzC0fUfsnUCex8\nZCUiQkNrFxsP1PHtV3bRGsEEIW1dvZQVODkWocmZPSeaSbBZQl6eZ6w53thOR3dvzLk9xgIzgo0z\nRhqj+Q+3zBuxce2nP6IgM9nBZ+ZP5Lf3LmWKDsCPFJ4m5MaKc129zCkauwUP4aKxrZsOLyXkDb4x\nBjbOqD7rP0azIN0ZEsMwKz+N//2ZOaPuJ1jy05ycPRfZSgtbjzawuCQz6qIsAmFZaTYZSaaCQTAY\nAxtHdPf2jSjZdmtnT8iKGC6dns3lM3P8NwwDFgtMiIJqt5uPNNDa0UNxVmRH88Gyoiwv0iqMW4yB\njSPeP3B6RD7R5+9ewoy80FVOvS5AV0OoONHYwekIj2D7OdfVS59S4y4xd5LDypIS78ugDb4xBjaO\n+OUHR/22mV+c4TckK1BmF0SuzHVNQ3vE/cD91DS0My0nMqvLgmXtpdPivkz5aDAGNk54d1+d1yqz\n7lxbEdosWwATMyNn4Lp7Fblp0RE2BgwkyRkPlGQnc/uS4kirMa4ZP/9tQ9C8/nEt9/76Q7/tJmYm\ncsMFoX+cT3ZYuWNJMb/a9EnI+x4J0ZTDtCtKQ7Zm5afylWVTuXRmDscb2mlo66K5o4cJKeN3ci4a\nMAY2Dvintw/6LfeRneLgX9dUkheG0Z5NZ+4aSwO7cHLmQH7ao2fbsFuE7r7I1zw5NKSOWDRwdXk+\nT39h4cB+tjGqIcO4CGKcpvbuEVVZfer2BczKD1+i6IXFmRRlhDfjVkqCjeKsJBZNcRnXRVMyqShM\nY0KyIyqMK7gmuyZlRVfmsfx0pynJHSaMgY1x0hPtLJ7iO1HH1OxkloQ5mYfFIiyc7HnpaGaSndGs\nBxBxJSNRSvFJfRtbql0j1y3VDew60czR+sglffFEXmr0+IQBfvGn6rgo2hgJjIGNA25a6DsT/Y9X\nXxD2SqzvHzjNH6vODJOnOW38du1S/vTQlTy4YkZQk0C5qQlsPlIf0WW57nXAHDYL6Yl2r237onC0\nWHU6+lwXsYDxwcYBbT4Mz+QJScydGPoKs+786dAZ7vy3zQx9Sp+UlchDV89mqg5deuDKUlYvmsQ9\nz29j+7GRZf1y2i0UZyWNeQUDd6bnpvC7+z7Fnw+d5fS5Tq6dU0B6op2n3zvE91/fN6x9dQTTKHpj\nw+ZPuKQ0Z8wLRsY6ZgQbB3zi4xG5vDC8Bfq2VNfzhWeHG9ebFkzk9a9fwnVDii/mpjl54S8u5OaF\nE0fUf2dPHztrAk/BGEpuXjiRlAQby8vyuG1x8cDo9csXl3j0O9e3dZEfRaFj4DL6xg8beoyBjXPO\nngtNqWxP1Ld2cef6zfS6WVe7VfiX2+bzw8/NI8VLhdIEm5Unb5rL2sum+b2GUq54zbHGbhVWluXx\n3RsruNVLOkeb1cK662YjAt+6dhab113JgmLX00Ikqy0MxWGz8OydldiiMGn6eMd8onFAc7v3ONBF\nfibARkNWsoPvrKoYJOvuVSzwMtnljsUifPPqWXzr2ll+26Ynjn2+gfsvL+WZL1by+Qsnk+kj38G1\ncwr42+vK+MqyqeSmOvn1Vy7k8pk5nIyCAoKJdisi8PLapRFdDBLL+DWwIrJeROpEZNcQ+ddEZL+I\n7BaRJ93kD4tIlT52lZv8ai2rEpGH3OQlIrJJRA6KyAsi4tDyBL1fpY9PCcUbjkfqWz37J798cQnf\nuGpmWK9908KJ/P1nz2fUSnXayPAxATSU25dM9luCu6Vj7BcS/Ofuk3T2jGxS7UufmjIwiei0W/nB\nLfP4q+UzwqmeT2bkpfD7By7mubsWs/uxq6iIgZSK0cpIJrl+ATwF/LJfICKXA6uAuUqpThHJ1fIy\nYDVQDhQCb4lI/530E2AFUANsEZFXlVJ7gCeAHymlNojI08DdwM/03wal1HQRWa3b3TraNxyPZA5J\nNeewWvjh5+Zx/dzQL4v1xG2Li2nt7GHfyRaumJVLshfXgCdSEmy8/deX8tGxRmbkpbDulV0DCwjA\nZSyOjiAFY6jZW9vMO3vruGaO/89waIRGdkoCn10wkcde2zPmPw4VRWm8et/FEc2TG0/4vdOVUu97\nGD2uBb6vlOrUbeq0fBWwQcuPiEgVsFgfq1JKHQYQkQ3AKhHZC1wB3K7bPAc8isvArtLbAC8BT4mI\nKOOJD5izrYP9rI/cUMan541thqsvL5sa9LmTspKYlJVEfWsXH33SMOjYgVPnWDQlcyD2dawoTHeS\nkxr8iieLRSgvTOPPh0de2Xe0OGwWvrOqwhjXMSRYH+wMYJl+dH9PRBZpeRFwzK1djZZ5k08AGpVS\nPUPkg/rSx5t0e0OAdPb0YrUIK8ryWH9nJXcsGZ8F7DIS7VxcOjy3bCRCtD5/0WQqR+m/vnRGboi0\n8c+EZAf/duciFhTHRp2w8UKwcbA2IBO4EFgEvCgiUwFPP40Kz4Zc+WiPn2ODEJF7gHsAiotN9p+h\nPHfXYhxWS9gXE4Qbi0V46vb57DzWxFf/fRvTcpMRhPYwLTAQgZyUBOpazhvwZaXZfHpeIbeMMIzM\nF+EOkQOwCNx/+XTuXjbV5+IHQ3gI1sDWAC/rx/XNItIHZGu5e8zKROCE3vYkPwNkiIhNj1Ld2/f3\nVSMiNiAd8Pg8pZR6BngGoLKy0rgQhhBLFUHTnHb6lCI72cGOY01hu87jq8pZXpbHnhPNfO8Pe0HB\n15eXck1FAQ5baIJvQpnU3Bt3faqEB1eGdyLT4J1gDezvcPlON+pJLAcuY/kq8GsR+Udck1ylwGZc\no9FSESkBjuOaCLtdKaVE5F3gZmADsAb4D32NV/X+B/r4O8b/ath1vIkvrt8ctv4tAjfOL+LWRcU4\nbBYK0hO5cnZ4Sqbkpzu5bXExHxw6Q/XZ8KzuWjrdeNUiyUjCtH6Dy8jNFJEaEbkbWA9M1aFbG4A1\nysVu4EVgD/CfwH1KqV49Or0feAPYC7yo2wJ8E3hQT4hNAJ7V8meBCVr+IDAQ2mWIX2bmp7J0WviM\nxtrLpvGPn7sgZKNUf/z9Z+eE9VpZySb1YCSRWBsUVlZWqq1bt0ZaDUMYOXT6HCt/9P6gFWKhIiPJ\nzn//r8tJdY6Nv7Krp4+KR96gK0RFJt3JSLLz4bdXmKiBMCAi25RSlf7amZVchnHH1OzkYbG9oaKx\nrXtElXdDRX1rV1iMK7jeSyQWYRjOYwysYdzR2dPHDfMKQ575KdFu5bEbylkc5ty47uSnOylI956X\nwFsO3ZFgswinz0V+SW48YwysYdzhtFu5bm5ByF0ET948lzVLp4x5ONvls4bHw1oEHrpmFr9du5TH\nbigPqt+CDCdFGSbHQCQxBtYwLjnR2E6o7eDkCJX39lRN4oErS/nqpa5sYmuWTuGxG8px2gP7uh6r\nb2f9/xwJiY6G4DAG1jAueWdfnd9CjoFwTUU+cyKU9KQ0d3A87MTMRB64onSQbM3SKSPOkevO67tq\nwzIZaBgZxsAaxiVXleeTaB/9AopJWYlcODWL6+YWRGyl28z81EF5DVaU5Xmc+Q9mae2u48189/d7\nTDLtCGFKxhjGJSvL8tjxyEpe31XL/pMtTM1JYeP+OvLSnDz7x5E/Frd09PB315dTNgbLVr1htQhL\nSrJ4bWctSQ4r91423WO7tCDqlQH8+XA9Z1u7TDnuCGAMrGFcYrEIDouw6oLzBR1vXjiRk00dnD3X\nye+2n/BxtguH1cK3rpkdUePazyUzcnhtZy2leales3QdORN4WkaH1cLTn19gjGuEMAbWEFPkpzv5\n/k1z2XeyhX0nW7y2u6Yin0c+XU6+jxCpsaRSh2PN9eEHzkp28NaDl5CX5uQPH9fyzPuHOXR6sNG1\nCBRlJpJktzElO4kHrixl8oSxL6ljcGEMrCHmcNqtXDIjx6OBtVuFb19XxhcvmhxV2cVKspN55gsL\nfSaAWVmeP7B966JibpxfxLIn3qVPwZyiNK6cncfK8jxyU6PjR8NglsoaYpTGti5uefoDDtadG5A9\ncdMcVpTlk+WjhtZ4o7mjm9QEW1T9WMQDZqmsIa7JSHLw0zsWDOwXZyVx88JJMWVcwZW+0RjX6MW4\nCAwxS2leKp+eV8jh0+e4fm7ol9YaDP4wBtYQ0/zLbfMjrYIhjjEuAoPBYAgTxsAaDAZDmDAG1mAw\nGMKEMbAGg8EQJoyBNRgMhjBhDKzBYDCECWNgDQaDIUwYA2swGAxhwhhYg8FgCBMxl+xFRE4DRyOt\nhx+ygTORViIAjL7hYzzpCkbffiYrpXL8NYo5AzseEJGtI8nEEy0YfcPHeNIVjL6BYlwEBoPBECaM\ngTUYDIYwYQxsZHgm0goEiNE3fIwnXcHoGxDGB2swGAxhwoxgDQaDIUwYAxsgIlItIh+LyHYR2apl\nt4jIbhHpE5HKIe0fFpEqEdkvIle5ya/WsioRechNXiIim0TkoIi8ICIOLU/Q+1X6+JRR6PsDEdkn\nIjtF5BURyYhyfR/Xum4Xkf8SkUItFxH5Z32NnSKywK2fNVqngyKyxk2+UPdfpc8VLc8SkTd1+zdF\nJDMYXd2OfUNEBJoUqwAABBNJREFUlIhkR4OuPj7bR0XkuJZtF5Fr3dpH3b2g5V/T198tIk9Gi74e\nUUqZVwAvoBrIHiKbDcwENgKVbvIyYAeQAJQAhwCrfh0CpgIO3aZMn/MisFpvPw2s1dv3Ak/r7dXA\nC6PQdyVg09tPAE9Eub5pbtsPuPV7LfA6IMCFwCYtzwIO67+ZejtTH9sMXKTPeR24RsufBB7S2w/1\nfyaB6qrlk4A3cMVjZ0eDrj4+20eBb3hoG633wuXAW0CC3s+NFn09vodgT4zXl7cvlT62kcEG9mHg\nYbf9N/QX5iLgjaHt9BfpDOeN30C7/nP1tk23k9Hoq49/BvjVONL3YeBnevvnwG1ux/YDBcBtwM/d\n5D/XsgJgn5t8oF3/uXq7ANgfrK7AS8A89+OR1tWbvng3sFF5L+AyisujUV9PL+MiCBwF/JeIbBOR\ne/y0LQKOue3XaJk3+QSgUSnVM0Q+qC99vEm3H62+d+EaHUW1viLyPRE5BtwB/F2Q+hbp7aFygDyl\nVK3WtxbIDUZXEbkBOK6U2jGkbaR19aiv5n7ttljv5m6I1nthBrBMP7q/JyKLokjfYZiih4HzKaXU\nCRHJBd4UkX1Kqfe9tPVUxlTh2fetfLT31Zc/vOorIuuAHuBX0a6vUmodsE5EHgbuBx7xcY1A5cEy\nTFdgHS4XzFAirSt41vdnwOO678eBH+L60Y3KewGXzcrE5WZZBLwoIlOjRN9hmBFsgCilTui/dcAr\nwGIfzWtw+eP6mQic8CE/A2SIiG2IfFBf+ng6UB+svnoy5XrgDqWfhaJZXzd+DdwUpL41enuoHOCU\niBRofQuAuiB0vRSX/2+HiFTr/j8UkfxI6+pF38VKqVNKqV6lVB/wfzj/eUfrvVADvKxcbAb6cOUb\niLi+3t6EeY3wBSQDqW7bfwKudju+kcE+2HIGO94P43K62/R2Cecd7+X6nP/LYMf7vXr7PgY73l8M\nVl/92gPkDGkfrfqWurX5GvCS3r6OwRNHm7U8CziCa6STqbez9LEtum3/xNG1Wv4DBk8cPTmae0HL\nqznvg42Yrn4+2wK3Nn8FbIjye+GrwHe0fAauR3mJtL5e38dYGqjx/sI1E7lDv3YD67T8M7h+9TqB\nUwx2qq/DNYu5Hz0LrOXXAgf0sXVDrrEZqNI3QP9sqVPvV+njU0ehb5W+Mbfr19NRru9vgV3ATuD/\nAUVaLsBPtE4fM/jH7S597SrgS27ySt3XIeApzi+2mQC8DRzUf7OC0XVIm2rOG9iI6erns31e67MT\neJXBBjca7wUH8O/6c/kQuCIa9PX2Miu5DAaDIUwYH6zBYDCECWNgDQaDIUwYA2swGAxhwhhYg8Fg\nCBPGwBoMBkOYMAbWYDAYwoQxsAaDwRAmjIE1GAyGMPH/ASzJ95vsu0FWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1185ab748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now let's preview what our map looks like with no data in it\n",
    "map_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "      <th>borough</th>\n",
       "      <th>Inner/_Outer_London</th>\n",
       "      <th>GLA_Population_Estimate_2017</th>\n",
       "      <th>GLA_Household_Estimate_2017</th>\n",
       "      <th>Inland_Area_(Hectares)</th>\n",
       "      <th>Population_density_(per_hectare)_2017</th>\n",
       "      <th>Average_Age,_2017</th>\n",
       "      <th>Proportion_of_population_aged_0-15,_2015</th>\n",
       "      <th>Proportion_of_population_of_working-age,_2015</th>\n",
       "      <th>...</th>\n",
       "      <th>Happiness_score_2011-14_(out_of_10)</th>\n",
       "      <th>Anxiety_score_2011-14_(out_of_10)</th>\n",
       "      <th>Childhood_Obesity_Prevalance_(%)_2015/16</th>\n",
       "      <th>People_aged_17+_with_diabetes_(%)</th>\n",
       "      <th>Mortality_rate_from_causes_considered_preventable_2012/14</th>\n",
       "      <th>Political_control_in_council</th>\n",
       "      <th>Proportion_of_seats_won_by_Conservatives_in_2014_election</th>\n",
       "      <th>Proportion_of_seats_won_by_Labour_in_2014_election</th>\n",
       "      <th>Proportion_of_seats_won_by_Lib_Dems_in_2014_election</th>\n",
       "      <th>Turnout_at_2014_local_elections</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E09000001</td>\n",
       "      <td>City of London</td>\n",
       "      <td>Inner London</td>\n",
       "      <td>8800.0</td>\n",
       "      <td>5326.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>30.3</td>\n",
       "      <td>43.2</td>\n",
       "      <td>11.4</td>\n",
       "      <td>73.1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>null</td>\n",
       "      <td>2.6</td>\n",
       "      <td>129</td>\n",
       "      <td>null</td>\n",
       "      <td>null</td>\n",
       "      <td>null</td>\n",
       "      <td>null</td>\n",
       "      <td>null</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E09000002</td>\n",
       "      <td>Barking and Dagenham</td>\n",
       "      <td>Outer London</td>\n",
       "      <td>209000.0</td>\n",
       "      <td>78188.0</td>\n",
       "      <td>3611.0</td>\n",
       "      <td>57.9</td>\n",
       "      <td>32.9</td>\n",
       "      <td>27.2</td>\n",
       "      <td>63.1</td>\n",
       "      <td>...</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>28.5</td>\n",
       "      <td>7.3</td>\n",
       "      <td>228</td>\n",
       "      <td>Lab</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E09000003</td>\n",
       "      <td>Barnet</td>\n",
       "      <td>Outer London</td>\n",
       "      <td>389600.0</td>\n",
       "      <td>151423.0</td>\n",
       "      <td>8675.0</td>\n",
       "      <td>44.9</td>\n",
       "      <td>37.3</td>\n",
       "      <td>21.1</td>\n",
       "      <td>64.9</td>\n",
       "      <td>...</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>20.7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>134</td>\n",
       "      <td>Cons</td>\n",
       "      <td>50.8</td>\n",
       "      <td>.</td>\n",
       "      <td>1.6</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E09000004</td>\n",
       "      <td>Bexley</td>\n",
       "      <td>Outer London</td>\n",
       "      <td>244300.0</td>\n",
       "      <td>97736.0</td>\n",
       "      <td>6058.0</td>\n",
       "      <td>40.3</td>\n",
       "      <td>39.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>62.9</td>\n",
       "      <td>...</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>22.7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>164</td>\n",
       "      <td>Cons</td>\n",
       "      <td>71.4</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0</td>\n",
       "      <td>39.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E09000005</td>\n",
       "      <td>Brent</td>\n",
       "      <td>Outer London</td>\n",
       "      <td>332100.0</td>\n",
       "      <td>121048.0</td>\n",
       "      <td>4323.0</td>\n",
       "      <td>76.8</td>\n",
       "      <td>35.6</td>\n",
       "      <td>20.9</td>\n",
       "      <td>67.8</td>\n",
       "      <td>...</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>24.3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>169</td>\n",
       "      <td>Lab</td>\n",
       "      <td>9.5</td>\n",
       "      <td>88.9</td>\n",
       "      <td>1.6</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Code               borough Inner/_Outer_London  \\\n",
       "0  E09000001        City of London        Inner London   \n",
       "1  E09000002  Barking and Dagenham        Outer London   \n",
       "2  E09000003                Barnet        Outer London   \n",
       "3  E09000004                Bexley        Outer London   \n",
       "4  E09000005                 Brent        Outer London   \n",
       "\n",
       "   GLA_Population_Estimate_2017  GLA_Household_Estimate_2017  \\\n",
       "0                        8800.0                       5326.0   \n",
       "1                      209000.0                      78188.0   \n",
       "2                      389600.0                     151423.0   \n",
       "3                      244300.0                      97736.0   \n",
       "4                      332100.0                     121048.0   \n",
       "\n",
       "   Inland_Area_(Hectares)  Population_density_(per_hectare)_2017  \\\n",
       "0                   290.0                                   30.3   \n",
       "1                  3611.0                                   57.9   \n",
       "2                  8675.0                                   44.9   \n",
       "3                  6058.0                                   40.3   \n",
       "4                  4323.0                                   76.8   \n",
       "\n",
       "   Average_Age,_2017  Proportion_of_population_aged_0-15,_2015  \\\n",
       "0               43.2                                      11.4   \n",
       "1               32.9                                      27.2   \n",
       "2               37.3                                      21.1   \n",
       "3               39.0                                      20.6   \n",
       "4               35.6                                      20.9   \n",
       "\n",
       "   Proportion_of_population_of_working-age,_2015  \\\n",
       "0                                           73.1   \n",
       "1                                           63.1   \n",
       "2                                           64.9   \n",
       "3                                           62.9   \n",
       "4                                           67.8   \n",
       "\n",
       "                ...                Happiness_score_2011-14_(out_of_10)  \\\n",
       "0               ...                                                6.0   \n",
       "1               ...                                                7.1   \n",
       "2               ...                                                7.4   \n",
       "3               ...                                                7.2   \n",
       "4               ...                                                7.2   \n",
       "\n",
       "   Anxiety_score_2011-14_(out_of_10)  \\\n",
       "0                                5.6   \n",
       "1                                3.1   \n",
       "2                                2.8   \n",
       "3                                3.3   \n",
       "4                                2.9   \n",
       "\n",
       "   Childhood_Obesity_Prevalance_(%)_2015/16  \\\n",
       "0                                      null   \n",
       "1                                      28.5   \n",
       "2                                      20.7   \n",
       "3                                      22.7   \n",
       "4                                      24.3   \n",
       "\n",
       "   People_aged_17+_with_diabetes_(%)  \\\n",
       "0                                2.6   \n",
       "1                                7.3   \n",
       "2                                6.0   \n",
       "3                                6.9   \n",
       "4                                7.9   \n",
       "\n",
       "  Mortality_rate_from_causes_considered_preventable_2012/14  \\\n",
       "0                                                129          \n",
       "1                                                228          \n",
       "2                                                134          \n",
       "3                                                164          \n",
       "4                                                169          \n",
       "\n",
       "  Political_control_in_council  \\\n",
       "0                         null   \n",
       "1                          Lab   \n",
       "2                         Cons   \n",
       "3                         Cons   \n",
       "4                          Lab   \n",
       "\n",
       "   Proportion_of_seats_won_by_Conservatives_in_2014_election  \\\n",
       "0                                               null           \n",
       "1                                                  0           \n",
       "2                                               50.8           \n",
       "3                                               71.4           \n",
       "4                                                9.5           \n",
       "\n",
       "  Proportion_of_seats_won_by_Labour_in_2014_election  \\\n",
       "0                                               null   \n",
       "1                                                100   \n",
       "2                                                  .   \n",
       "3                                               23.8   \n",
       "4                                               88.9   \n",
       "\n",
       "   Proportion_of_seats_won_by_Lib_Dems_in_2014_election  \\\n",
       "0                                               null      \n",
       "1                                                  0      \n",
       "2                                                1.6      \n",
       "3                                                  0      \n",
       "4                                                1.6      \n",
       "\n",
       "  Turnout_at_2014_local_elections  \n",
       "0                            null  \n",
       "1                            36.5  \n",
       "2                            40.5  \n",
       "3                            39.6  \n",
       "4                            36.3  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load in new csv file\n",
    "df = pd.read_csv(\"datasets/london-borough-profile.csv\", header=0)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>borough</th>\n",
       "      <th>happiness</th>\n",
       "      <th>anxiety</th>\n",
       "      <th>pop_density_per_hectare</th>\n",
       "      <th>mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>City of London</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>30.3</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barking and Dagenham</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>57.9</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>44.9</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>40.3</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brent</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>76.8</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                borough  happiness  anxiety  pop_density_per_hectare  \\\n",
       "0        City of London        6.0      5.6                     30.3   \n",
       "1  Barking and Dagenham        7.1      3.1                     57.9   \n",
       "2                Barnet        7.4      2.8                     44.9   \n",
       "3                Bexley        7.2      3.3                     40.3   \n",
       "4                 Brent        7.2      2.9                     76.8   \n",
       "\n",
       "   mortality  \n",
       "0        129  \n",
       "1        228  \n",
       "2        134  \n",
       "3        164  \n",
       "4        169  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select only the coluns that we want for the map\n",
    "df = df[['borough','Happiness_score_2011-14_(out_of_10)', 'Anxiety_score_2011-14_(out_of_10)', 'Population_density_(per_hectare)_2017', 'Mortality_rate_from_causes_considered_preventable_2012/14']]\n",
    "\n",
    "# those are really terrible column names. let's rename them to something simpler\n",
    "score = df.rename(index=str, columns={\"Happiness_score_2011-14_(out_of_10)\": \"happiness\",\n",
    "                                      \"Anxiety_score_2011-14_(out_of_10)\": \"anxiety\",\n",
    "                                      \"Population_density_(per_hectare)_2017\": \"pop_density_per_hectare\",\n",
    "                                      \"Mortality_rate_from_causes_considered_preventable_2012/14\": 'mortality'})\n",
    "\n",
    "# check dat dataframe\n",
    "score.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GSS_CODE</th>\n",
       "      <th>HECTARES</th>\n",
       "      <th>NONLD_AREA</th>\n",
       "      <th>ONS_INNER</th>\n",
       "      <th>SUB_2009</th>\n",
       "      <th>SUB_2006</th>\n",
       "      <th>geometry</th>\n",
       "      <th>happiness</th>\n",
       "      <th>anxiety</th>\n",
       "      <th>pop_density_per_hectare</th>\n",
       "      <th>mortality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kingston upon Thames</th>\n",
       "      <td>E09000021</td>\n",
       "      <td>3726.117</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((516401.6 160201.8, 516407.3 160210.5...</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>47.1</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croydon</th>\n",
       "      <td>E09000008</td>\n",
       "      <td>8649.441</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((535009.2 159504.7, 535005.5 159502, ...</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>44.7</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bromley</th>\n",
       "      <td>E09000006</td>\n",
       "      <td>15013.487</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((540373.6 157530.4, 540361.2 157551.9...</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>21.8</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hounslow</th>\n",
       "      <td>E09000018</td>\n",
       "      <td>5658.541</td>\n",
       "      <td>60.755</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((521975.8 178100, 521967.7 178096.8, ...</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>49.0</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ealing</th>\n",
       "      <td>E09000009</td>\n",
       "      <td>5554.428</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((510253.5 182881.6, 510249.9 182886, ...</td>\n",
       "      <td>7.3</td>\n",
       "      <td>3.6</td>\n",
       "      <td>63.3</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       GSS_CODE   HECTARES  NONLD_AREA ONS_INNER SUB_2009  \\\n",
       "NAME                                                                        \n",
       "Kingston upon Thames  E09000021   3726.117       0.000         F     None   \n",
       "Croydon               E09000008   8649.441       0.000         F     None   \n",
       "Bromley               E09000006  15013.487       0.000         F     None   \n",
       "Hounslow              E09000018   5658.541      60.755         F     None   \n",
       "Ealing                E09000009   5554.428       0.000         F     None   \n",
       "\n",
       "                     SUB_2006  \\\n",
       "NAME                            \n",
       "Kingston upon Thames     None   \n",
       "Croydon                  None   \n",
       "Bromley                  None   \n",
       "Hounslow                 None   \n",
       "Ealing                   None   \n",
       "\n",
       "                                                               geometry  \\\n",
       "NAME                                                                      \n",
       "Kingston upon Thames  POLYGON ((516401.6 160201.8, 516407.3 160210.5...   \n",
       "Croydon               POLYGON ((535009.2 159504.7, 535005.5 159502, ...   \n",
       "Bromley               POLYGON ((540373.6 157530.4, 540361.2 157551.9...   \n",
       "Hounslow              POLYGON ((521975.8 178100, 521967.7 178096.8, ...   \n",
       "Ealing                POLYGON ((510253.5 182881.6, 510249.9 182886, ...   \n",
       "\n",
       "                      happiness  anxiety  pop_density_per_hectare  mortality  \n",
       "NAME                                                                          \n",
       "Kingston upon Thames        7.4      3.3                     47.1        141  \n",
       "Croydon                     7.2      3.3                     44.7        178  \n",
       "Bromley                     7.4      3.3                     21.8        148  \n",
       "Hounslow                    7.4      3.4                     49.0        166  \n",
       "Ealing                      7.3      3.6                     63.3        164  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join the geodataframe with the cleaned up csv dataframe\n",
    "merged = map_df.set_index('NAME').join(score.set_index('borough'))\n",
    "\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj8AAAGLCAYAAAAoH/PJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEv4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122488940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set a variable that will call whatever column we want to visualise on the map\n",
    "variable = 'mortality'\n",
    "\n",
    "# set the range for the choropleth\n",
    "vmin, vmax = 120, 220\n",
    "\n",
    "# create figure and axes for Matplotlib\n",
    "fig, ax = plt.subplots(1, figsize=(10, 6))\n",
    "\n",
    "# create map\n",
    "merged.plot(column=variable, cmap='Blues', linewidth=0.8, ax=ax, edgecolor='0.8')\n",
    "\n",
    "# Now we can customise and add annotations\n",
    "\n",
    "# remove the axis\n",
    "ax.axis('off')\n",
    "\n",
    "# add a title\n",
    "ax.set_title('Preventable death rate in London', \\\n",
    "              fontdict={'fontsize': '25',\n",
    "                        'fontweight' : '3'})\n",
    "\n",
    "# create an annotation for the  data source\n",
    "ax.annotate('Source: London Datastore, 2014',\n",
    "           xy=(0.1, .08), xycoords='figure fraction',\n",
    "           horizontalalignment='left', verticalalignment='top',\n",
    "           fontsize=10, color='#555555')\n",
    "\n",
    "# Create colorbar as a legend\n",
    "sm = plt.cm.ScalarMappable(cmap='Blues', norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "sm._A = []\n",
    "cbar = fig.colorbar(sm)\n",
    "\n",
    "# this will save the figure as a high-res png. you can also save as svg\n",
    "fig.savefig('testmap.png', dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
